{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.10"
    },
    "colab": {
      "name": "12_Predicting_Ki_of_Ligands_to_a_Protein.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNXzKyg2eYtR",
        "colab_type": "text"
      },
      "source": [
        "# Tutorial Part 12: Predicting Ki of Ligands to a Protein\n",
        "\n",
        "\n",
        "In this notebook, we analyze the BACE enyzme and build machine learning models for predicting the Ki of ligands to the protein. We will use the `deepchem` library to load this data into memory, split into train/test/validation folds, build and cross-validate models, and report statistics.\n",
        "\n",
        "## Colab\n",
        "\n",
        "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/12_Predicting_Ki_of_Ligands_to_a_Protein.ipynb)\n",
        "\n",
        "## Setup\n",
        "\n",
        "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xoDXdhhYfKmD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "f66828d4-75c5-451a-8246-9c536eb12cbc"
      },
      "source": [
        "!curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
        "import conda_installer\n",
        "conda_installer.install()\n",
        "!/root/miniconda/bin/conda info -e"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  3489  100  3489    0     0  38340      0 --:--:-- --:--:-- --:--:-- 38340\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
            "all packages is already installed\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "# conda environments:\n",
            "#\n",
            "base                  *  /root/miniconda\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a29LY7K_CdOl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        },
        "outputId": "022d1106-c1ee-4e9f-a3ed-50d8514a05ca"
      },
      "source": [
        "!pip install --pre deepchem\n",
        "import deepchem\n",
        "deepchem.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: deepchem in /usr/local/lib/python3.6/dist-packages (2.4.0rc1.dev20200805143534)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from deepchem) (0.16.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from deepchem) (0.22.2.post1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from deepchem) (1.0.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from deepchem) (1.18.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from deepchem) (1.4.1)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->deepchem) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->deepchem) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.6.1->pandas->deepchem) (1.15.0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.4.0-rc1.dev'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9uKkg6iXeYtb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "30eb36c2-4743-46a6-d996-a45aba7188af"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "import deepchem as dc\n",
        "from deepchem.utils.save import load_from_disk\n",
        "\n",
        "current_dir = os.path.dirname(os.path.realpath(\"__file__\"))\n",
        "dc.utils.download_url(\"https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/desc_canvas_aug30.csv\",\n",
        "                      current_dir)\n",
        "dataset_file = \"desc_canvas_aug30.csv\"\n",
        "dataset = load_from_disk(dataset_file)\n",
        "num_display=10\n",
        "pretty_columns = (\n",
        "    \"[\" + \",\".join([\"'%s'\" % column for column in dataset.columns.values[:num_display]])\n",
        "    + \",...]\")\n",
        "\n",
        "dc.utils.download_url(\"https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/crystal_desc_canvas_aug30.csv\",\n",
        "                      current_dir)\n",
        "crystal_dataset_file = \"crystal_desc_canvas_aug30.csv\"\n",
        "crystal_dataset = load_from_disk(crystal_dataset_file)\n",
        "\n",
        "print(\"Columns of dataset: %s\" % pretty_columns)\n",
        "print(\"Number of examples in dataset: %s\" % str(dataset.shape[0]))\n",
        "print(\"Number of examples in crystal dataset: %s\" % str(crystal_dataset.shape[0]))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Columns of dataset: ['mol','CID','Class','Model','pIC50','MW','AlogP','HBA','HBD','RB',...]\n",
            "Number of examples in dataset: 1522\n",
            "Number of examples in crystal dataset: 25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fX2Dy785eYtp",
        "colab_type": "text"
      },
      "source": [
        "To gain a visual understanding of compounds in our dataset, let's draw them using rdkit. We define a couple of helper functions to get started."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxN6zSo8eYts",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tempfile\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Draw\n",
        "from itertools import islice\n",
        "from IPython.display import Image, display, HTML\n",
        "\n",
        "def display_images(filenames):\n",
        "    \"\"\"Helper to pretty-print images.\"\"\"\n",
        "    for filename in filenames:\n",
        "        display(Image(filename))\n",
        "\n",
        "def mols_to_pngs(mols, basename=\"test\"):\n",
        "    \"\"\"Helper to write RDKit mols to png files.\"\"\"\n",
        "    filenames = []\n",
        "    for i, mol in enumerate(mols):\n",
        "        filename = \"BACE_%s%d.png\" % (basename, i)\n",
        "        Draw.MolToFile(mol, filename)\n",
        "        filenames.append(filename)\n",
        "    return filenames"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qnqxVm8ceYtw",
        "colab_type": "text"
      },
      "source": [
        "Now, we display a compound from the dataset. Note the complex ring structures and polar structures."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEaaVKbKeYtz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "70133bcc-b508-4e30-f12a-17d9ad6e2651"
      },
      "source": [
        "num_to_display = 12\n",
        "molecules = []\n",
        "for _, data in islice(dataset.iterrows(), num_to_display):\n",
        "    molecules.append(Chem.MolFromSmiles(data[\"mol\"]))\n",
        "display_images(mols_to_pngs(molecules, basename=\"dataset\"))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lrEcrEsOeYt5",
        "colab_type": "text"
      },
      "source": [
        "Now let's picture the compounds in the crystal structure collection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBa2xXeNeYt7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b296c862-8c40-4dd3-b2f7-0baa96ffcc17"
      },
      "source": [
        "num_to_display = 12\n",
        "molecules = []\n",
        "for _, data in islice(crystal_dataset.iterrows(), num_to_display):\n",
        "    molecules.append(Chem.MolFromSmiles(data[\"mol\"]))\n",
        "display_images(mols_to_pngs(molecules, basename=\"crystal_dataset\"))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqbaG6ZEeYuE",
        "colab_type": "text"
      },
      "source": [
        "Analyzing the distribution of pIC50 values in the dataset gives us a nice spread."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_N2_csYeYuG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "156107c1-1b96-4508-f133-a897c3966021"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "pIC50s = np.array(dataset[\"pIC50\"])\n",
        "# Remove some dirty data from the dataset\n",
        "pIC50s = [pIC50 for pIC50 in pIC50s if pIC50 != '']\n",
        "n, bins, patches = plt.hist(pIC50s, 50, facecolor='green', alpha=0.75)\n",
        "plt.xlabel('Measured pIC50')\n",
        "plt.ylabel('Number of compounds')\n",
        "plt.title(r'Histogram of pIC50 Values')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sgobPzXteYuL",
        "colab_type": "text"
      },
      "source": [
        "We now featurize the data using the Canvas samples. To do so, we must specify the columns in the data input that correspond to the features. (Note that CanvasUID is excluded!)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lbo1SzuleYuN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "user_specified_features = ['MW','AlogP','HBA','HBD','RB','HeavyAtomCount','ChiralCenterCount','ChiralCenterCountAllPossible','RingCount','PSA','Estate','MR','Polar','sLi_Key','ssBe_Key','ssssBem_Key','sBH2_Key','ssBH_Key','sssB_Key','ssssBm_Key','sCH3_Key','dCH2_Key','ssCH2_Key','tCH_Key','dsCH_Key','aaCH_Key','sssCH_Key','ddC_Key','tsC_Key','dssC_Key','aasC_Key','aaaC_Key','ssssC_Key','sNH3_Key','sNH2_Key','ssNH2_Key','dNH_Key','ssNH_Key','aaNH_Key','tN_Key','sssNH_Key','dsN_Key','aaN_Key','sssN_Key','ddsN_Key','aasN_Key','ssssN_Key','daaN_Key','sOH_Key','dO_Key','ssO_Key','aaO_Key','aOm_Key','sOm_Key','sF_Key','sSiH3_Key','ssSiH2_Key','sssSiH_Key','ssssSi_Key','sPH2_Key','ssPH_Key','sssP_Key','dsssP_Key','ddsP_Key','sssssP_Key','sSH_Key','dS_Key','ssS_Key','aaS_Key','dssS_Key','ddssS_Key','ssssssS_Key','Sm_Key','sCl_Key','sGeH3_Key','ssGeH2_Key','sssGeH_Key','ssssGe_Key','sAsH2_Key','ssAsH_Key','sssAs_Key','dsssAs_Key','ddsAs_Key','sssssAs_Key','sSeH_Key','dSe_Key','ssSe_Key','aaSe_Key','dssSe_Key','ssssssSe_Key','ddssSe_Key','sBr_Key','sSnH3_Key','ssSnH2_Key','sssSnH_Key','ssssSn_Key','sI_Key','sPbH3_Key','ssPbH2_Key','sssPbH_Key','ssssPb_Key','sLi_Cnt','ssBe_Cnt','ssssBem_Cnt','sBH2_Cnt','ssBH_Cnt','sssB_Cnt','ssssBm_Cnt','sCH3_Cnt','dCH2_Cnt','ssCH2_Cnt','tCH_Cnt','dsCH_Cnt','aaCH_Cnt','sssCH_Cnt','ddC_Cnt','tsC_Cnt','dssC_Cnt','aasC_Cnt','aaaC_Cnt','ssssC_Cnt','sNH3_Cnt','sNH2_Cnt','ssNH2_Cnt','dNH_Cnt','ssNH_Cnt','aaNH_Cnt','tN_Cnt','sssNH_Cnt','dsN_Cnt','aaN_Cnt','sssN_Cnt','ddsN_Cnt','aasN_Cnt','ssssN_Cnt','daaN_Cnt','sOH_Cnt','dO_Cnt','ssO_Cnt','aaO_Cnt','aOm_Cnt','sOm_Cnt','sF_Cnt','sSiH3_Cnt','ssSiH2_Cnt','sssSiH_Cnt','ssssSi_Cnt','sPH2_Cnt','ssPH_Cnt','sssP_Cnt','dsssP_Cnt','ddsP_Cnt','sssssP_Cnt','sSH_Cnt','dS_Cnt','ssS_Cnt','aaS_Cnt','dssS_Cnt','ddssS_Cnt','ssssssS_Cnt','Sm_Cnt','sCl_Cnt','sGeH3_Cnt','ssGeH2_Cnt','sssGeH_Cnt','ssssGe_Cnt','sAsH2_Cnt','ssAsH_Cnt','sssAs_Cnt','dsssAs_Cnt','ddsAs_Cnt','sssssAs_Cnt','sSeH_Cnt','dSe_Cnt','ssSe_Cnt','aaSe_Cnt','dssSe_Cnt','ssssssSe_Cnt','ddssSe_Cnt','sBr_Cnt','sSnH3_Cnt','ssSnH2_Cnt','sssSnH_Cnt','ssssSn_Cnt','sI_Cnt','sPbH3_Cnt','ssPbH2_Cnt','sssPbH_Cnt','ssssPb_Cnt','sLi_Sum','ssBe_Sum','ssssBem_Sum','sBH2_Sum','ssBH_Sum','sssB_Sum','ssssBm_Sum','sCH3_Sum','dCH2_Sum','ssCH2_Sum','tCH_Sum','dsCH_Sum','aaCH_Sum','sssCH_Sum','ddC_Sum','tsC_Sum','dssC_Sum','aasC_Sum','aaaC_Sum','ssssC_Sum','sNH3_Sum','sNH2_Sum','ssNH2_Sum','dNH_Sum','ssNH_Sum','aaNH_Sum','tN_Sum','sssNH_Sum','dsN_Sum','aaN_Sum','sssN_Sum','ddsN_Sum','aasN_Sum','ssssN_Sum','daaN_Sum','sOH_Sum','dO_Sum','ssO_Sum','aaO_Sum','aOm_Sum','sOm_Sum','sF_Sum','sSiH3_Sum','ssSiH2_Sum','sssSiH_Sum','ssssSi_Sum','sPH2_Sum','ssPH_Sum','sssP_Sum','dsssP_Sum','ddsP_Sum','sssssP_Sum','sSH_Sum','dS_Sum','ssS_Sum','aaS_Sum','dssS_Sum','ddssS_Sum','ssssssS_Sum','Sm_Sum','sCl_Sum','sGeH3_Sum','ssGeH2_Sum','sssGeH_Sum','ssssGe_Sum','sAsH2_Sum','ssAsH_Sum','sssAs_Sum','dsssAs_Sum','ddsAs_Sum','sssssAs_Sum','sSeH_Sum','dSe_Sum','ssSe_Sum','aaSe_Sum','dssSe_Sum','ssssssSe_Sum','ddssSe_Sum','sBr_Sum','sSnH3_Sum','ssSnH2_Sum','sssSnH_Sum','ssssSn_Sum','sI_Sum','sPbH3_Sum','ssPbH2_Sum','sssPbH_Sum','ssssPb_Sum','sLi_Avg','ssBe_Avg','ssssBem_Avg','sBH2_Avg','ssBH_Avg','sssB_Avg','ssssBm_Avg','sCH3_Avg','dCH2_Avg','ssCH2_Avg','tCH_Avg','dsCH_Avg','aaCH_Avg','sssCH_Avg','ddC_Avg','tsC_Avg','dssC_Avg','aasC_Avg','aaaC_Avg','ssssC_Avg','sNH3_Avg','sNH2_Avg','ssNH2_Avg','dNH_Avg','ssNH_Avg','aaNH_Avg','tN_Avg','sssNH_Avg','dsN_Avg','aaN_Avg','sssN_Avg','ddsN_Avg','aasN_Avg','ssssN_Avg','daaN_Avg','sOH_Avg','dO_Avg','ssO_Avg','aaO_Avg','aOm_Avg','sOm_Avg','sF_Avg','sSiH3_Avg','ssSiH2_Avg','sssSiH_Avg','ssssSi_Avg','sPH2_Avg','ssPH_Avg','sssP_Avg','dsssP_Avg','ddsP_Avg','sssssP_Avg','sSH_Avg','dS_Avg','ssS_Avg','aaS_Avg','dssS_Avg','ddssS_Avg','ssssssS_Avg','Sm_Avg','sCl_Avg','sGeH3_Avg','ssGeH2_Avg','sssGeH_Avg','ssssGe_Avg','sAsH2_Avg','ssAsH_Avg','sssAs_Avg','dsssAs_Avg','ddsAs_Avg','sssssAs_Avg','sSeH_Avg','dSe_Avg','ssSe_Avg','aaSe_Avg','dssSe_Avg','ssssssSe_Avg','ddssSe_Avg','sBr_Avg','sSnH3_Avg','ssSnH2_Avg','sssSnH_Avg','ssssSn_Avg','sI_Avg','sPbH3_Avg','ssPbH2_Avg','sssPbH_Avg','ssssPb_Avg','First Zagreb (ZM1)','First Zagreb index by valence vertex degrees (ZM1V)','Second Zagreb (ZM2)','Second Zagreb index by valence vertex degrees (ZM2V)','Polarity (Pol)','Narumi Simple Topological (NST)','Narumi Harmonic Topological (NHT)','Narumi Geometric Topological (NGT)','Total structure connectivity (TSC)','Wiener (W)','Mean Wiener (MW)','Xu (Xu)','Quadratic (QIndex)','Radial centric (RC)','Mean Square Distance Balaban (MSDB)','Superpendentic (SP)','Harary (Har)','Log of product of row sums (LPRS)','Pogliani (Pog)','Schultz Molecular Topological (SMT)','Schultz Molecular Topological by valence vertex degrees (SMTV)','Mean Distance Degree Deviation (MDDD)','Ramification (Ram)','Gutman Molecular Topological (GMT)','Gutman MTI by valence vertex degrees (GMTV)','Average vertex distance degree (AVDD)','Unipolarity (UP)','Centralization (CENT)','Variation (VAR)','Molecular electrotopological variation (MEV)','Maximal electrotopological positive variation (MEPV)','Maximal electrotopological negative variation (MENV)','Eccentric connectivity (ECCc)','Eccentricity (ECC)','Average eccentricity (AECC)','Eccentric (DECC)','Valence connectivity index chi-0 (vX0)','Valence connectivity index chi-1 (vX1)','Valence connectivity index chi-2 (vX2)','Valence connectivity index chi-3 (vX3)','Valence connectivity index chi-4 (vX4)','Valence connectivity index chi-5 (vX5)','Average valence connectivity index chi-0 (AvX0)','Average valence connectivity index chi-1 (AvX1)','Average valence connectivity index chi-2 (AvX2)','Average valence connectivity index chi-3 (AvX3)','Average valence connectivity index chi-4 (AvX4)','Average valence connectivity index chi-5 (AvX5)','Quasi Wiener (QW)','First Mohar (FM)','Second Mohar (SM)','Spanning tree number (STN)','Kier benzene-likeliness index (KBLI)','Topological charge index of order 1 (TCI1)','Topological charge index of order 2 (TCI2)','Topological charge index of order 3 (TCI3)','Topological charge index of order 4 (TCI4)','Topological charge index of order 5 (TCI5)','Topological charge index of order 6 (TCI6)','Topological charge index of order 7 (TCI7)','Topological charge index of order 8 (TCI8)','Topological charge index of order 9 (TCI9)','Topological charge index of order 10 (TCI10)','Mean topological charge index of order 1 (MTCI1)','Mean topological charge index of order 2 (MTCI2)','Mean topological charge index of order 3 (MTCI3)','Mean topological charge index of order 4 (MTCI4)','Mean topological charge index of order 5 (MTCI5)','Mean topological charge index of order 6 (MTCI6)','Mean topological charge index of order 7 (MTCI7)','Mean topological charge index of order 8 (MTCI8)','Mean topological charge index of order 9 (MTCI9)','Mean topological charge index of order 10 (MTCI10)','Global topological charge (GTC)','Hyper-distance-path index (HDPI)','Reciprocal hyper-distance-path index (RHDPI)','Square reciprocal distance sum (SRDS)','Modified Randic connectivity (MRC)','Balaban centric (BC)','Lopping centric (LC)','Kier Hall electronegativity (KHE)','Sum of topological distances between N..N (STD(N N))','Sum of topological distances between N..O (STD(N O))','Sum of topological distances between N..S (STD(N S))','Sum of topological distances between N..P (STD(N P))','Sum of topological distances between N..F (STD(N F))','Sum of topological distances between N..Cl (STD(N Cl))','Sum of topological distances between N..Br (STD(N Br))','Sum of topological distances between N..I (STD(N I))','Sum of topological distances between O..O (STD(O O))','Sum of topological distances between O..S (STD(O S))','Sum of topological distances between O..P (STD(O P))','Sum of topological distances between O..F (STD(O F))','Sum of topological distances between O..Cl (STD(O Cl))','Sum of topological distances between O..Br (STD(O Br))','Sum of topological distances between O..I (STD(O I))','Sum of topological distances between S..S (STD(S S))','Sum of topological distances between S..P (STD(S P))','Sum of topological distances between S..F (STD(S F))','Sum of topological distances between S..Cl (STD(S Cl))','Sum of topological distances between S..Br (STD(S Br))','Sum of topological distances between S..I (STD(S I))','Sum of topological distances between P..P (STD(P P))','Sum of topological distances between P..F (STD(P F))','Sum of topological distances between P..Cl (STD(P Cl))','Sum of topological distances between P..Br (STD(P Br))','Sum of topological distances between P..I (STD(P I))','Sum of topological distances between F..F (STD(F F))','Sum of topological distances between F..Cl (STD(F Cl))','Sum of topological distances between F..Br (STD(F Br))','Sum of topological distances between F..I (STD(F I))','Sum of topological distances between Cl..Cl (STD(Cl Cl))','Sum of topological distances between Cl..Br (STD(Cl Br))','Sum of topological distances between Cl..I (STD(Cl I))','Sum of topological distances between Br..Br (STD(Br Br))','Sum of topological distances between Br..I (STD(Br I))','Sum of topological distances between I..I (STD(I I))','Wiener-type index from Z weighted distance matrix - Barysz matrix (WhetZ)','Wiener-type index from electronegativity weighted distance matrix (Whete)','Wiener-type index from mass weighted distance matrix (Whetm)','Wiener-type index from van der waals weighted distance matrix (Whetv)','Wiener-type index from polarizability weighted distance matrix (Whetp)','Balaban-type index from Z weighted distance matrix - Barysz matrix (JhetZ)','Balaban-type index from electronegativity weighted distance matrix (Jhete)','Balaban-type index from mass weighted distance matrix (Jhetm)','Balaban-type index from van der waals weighted distance matrix (Jhetv)','Balaban-type index from polarizability weighted distance matrix (Jhetp)','Topological diameter (TD)','Topological radius (TR)','Petitjean 2D shape (PJ2DS)','Balaban distance connectivity index (J)','Solvation connectivity index chi-0 (SCIX0)','Solvation connectivity index chi-1 (SCIX1)','Solvation connectivity index chi-2 (SCIX2)','Solvation connectivity index chi-3 (SCIX3)','Solvation connectivity index chi-4 (SCIX4)','Solvation connectivity index chi-5 (SCIX5)','Connectivity index chi-0 (CIX0)','Connectivity chi-1 [Randic connectivity] (CIX1)','Connectivity index chi-2 (CIX2)','Connectivity index chi-3 (CIX3)','Connectivity index chi-4 (CIX4)','Connectivity index chi-5 (CIX5)','Average connectivity index chi-0 (ACIX0)','Average connectivity index chi-1 (ACIX1)','Average connectivity index chi-2 (ACIX2)','Average connectivity index chi-3 (ACIX3)','Average connectivity index chi-4 (ACIX4)','Average connectivity index chi-5 (ACIX5)','reciprocal distance Randic-type index (RDR)','reciprocal distance square Randic-type index (RDSR)','1-path Kier alpha-modified shape index (KAMS1)','2-path Kier alpha-modified shape index (KAMS2)','3-path Kier alpha-modified shape index (KAMS3)','Kier flexibility (KF)','path/walk 2 - Randic shape index (RSIpw2)','path/walk 3 - Randic shape index (RSIpw3)','path/walk 4 - Randic shape index (RSIpw4)','path/walk 5 - Randic shape index (RSIpw5)','E-state topological parameter (ETP)','Ring Count 3 (RNGCNT3)','Ring Count 4 (RNGCNT4)','Ring Count 5 (RNGCNT5)','Ring Count 6 (RNGCNT6)','Ring Count 7 (RNGCNT7)','Ring Count 8 (RNGCNT8)','Ring Count 9 (RNGCNT9)','Ring Count 10 (RNGCNT10)','Ring Count 11 (RNGCNT11)','Ring Count 12 (RNGCNT12)','Ring Count 13 (RNGCNT13)','Ring Count 14 (RNGCNT14)','Ring Count 15 (RNGCNT15)','Ring Count 16 (RNGCNT16)','Ring Count 17 (RNGCNT17)','Ring Count 18 (RNGCNT18)','Ring Count 19 (RNGCNT19)','Ring Count 20 (RNGCNT20)','Atom Count (ATMCNT)','Bond Count (BNDCNT)','Atoms in Ring System (ATMRNGCNT)','Bonds in Ring System (BNDRNGCNT)','Cyclomatic number (CYCLONUM)','Number of ring systems (NRS)','Normalized number of ring systems (NNRS)','Ring Fusion degree (RFD)','Ring perimeter (RNGPERM)','Ring bridge count (RNGBDGE)','Molecule cyclized degree (MCD)','Ring Fusion density (RFDELTA)','Ring complexity index (RCI)','Van der Waals surface area (VSA)','MR1 (MR1)','MR2 (MR2)','MR3 (MR3)','MR4 (MR4)','MR5 (MR5)','MR6 (MR6)','MR7 (MR7)','MR8 (MR8)','ALOGP1 (ALOGP1)','ALOGP2 (ALOGP2)','ALOGP3 (ALOGP3)','ALOGP4 (ALOGP4)','ALOGP5 (ALOGP5)','ALOGP6 (ALOGP6)','ALOGP7 (ALOGP7)','ALOGP8 (ALOGP8)','ALOGP9 (ALOGP9)','ALOGP10 (ALOGP10)','PEOE1 (PEOE1)','PEOE2 (PEOE2)','PEOE3 (PEOE3)','PEOE4 (PEOE4)','PEOE5 (PEOE5)','PEOE6 (PEOE6)','PEOE7 (PEOE7)','PEOE8 (PEOE8)','PEOE9 (PEOE9)','PEOE10 (PEOE10)','PEOE11 (PEOE11)','PEOE12 (PEOE12)','PEOE13 (PEOE13)','PEOE14 (PEOE14)']"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "op-ucdRNeYuT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "4c9da6fd-dc51-494b-e4a9-72efc9ad9465"
      },
      "source": [
        "import deepchem as dc\n",
        "import tempfile, shutil\n",
        "\n",
        "featurizer = dc.feat.UserDefinedFeaturizer(user_specified_features)\n",
        "loader = dc.data.UserCSVLoader(\n",
        "      tasks=[\"Class\"], smiles_field=\"mol\", id_field=\"mol\",\n",
        "      featurizer=featurizer)\n",
        "dataset = loader.featurize(dataset_file)\n",
        "crystal_dataset = loader.featurize(crystal_dataset_file)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "smiles_field is deprecated and will be removed in a future version of DeepChem. Use feature_field instead.\n",
            "/usr/local/lib/python3.6/dist-packages/deepchem/data/data_loader.py:198: FutureWarning: featurize() is deprecated and has been renamed to create_dataset(). featurize() will be removed in DeepChem 3.0\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UAg_knFneYub",
        "colab_type": "text"
      },
      "source": [
        "This data is already split into three subsets \"Train\" and \"Test\" with 20% and 80% respectively of the total data from the BACE enzyme. There is also a \"Validation\" set that contains data from a separate (but related assay). (Note that these names are really misnomers. The \"Test\" set would be called a validation set in standard machine-learning practice and the \"Validation\" set would typically be called an external test set.) Hence, we will rename the datasets after loading them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XISgZKsYeYuc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "splitter = dc.splits.SpecifiedSplitter(dataset_file, \"Model\")\n",
        "train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(\n",
        "    dataset)\n",
        "#NOTE THE RENAMING:\n",
        "valid_dataset, test_dataset = test_dataset, valid_dataset"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ueVztyzeYuh",
        "colab_type": "text"
      },
      "source": [
        "Let's quickly take a look at a compound in the validation set. (The compound displayed earlier was drawn from the train set)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-l8uMJpueYuj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5f5b421f-32a8-4786-a102-68843b89ec79"
      },
      "source": [
        "print(valid_dataset.ids)\n",
        "valid_mols = [Chem.MolFromSmiles(compound)\n",
        "              for compound in islice(valid_dataset.ids, num_to_display)]\n",
        "display_images(mols_to_pngs(valid_mols, basename=\"valid_set\"))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)C(=O)NC(C(C)C)C(=O)NCc1ccccc1)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C'\n",
            " 'O=C(NCCC(C)(C)C)C(Cc1cc2cc(ccc2nc1N)-c1ccccc1C)C'\n",
            " 'Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)C(O)C1[NH2+]CC(O)C1'\n",
            " ... 'Brc1cc(ccc1)C1CC1C=1N=C(N)N(C)C(=O)C=1'\n",
            " 'O=C1N(C)C(=NC(=C1)C1CC1c1cc(ccc1)-c1ccccc1)N'\n",
            " 'Clc1cc2nc(n(c2cc1)CCCC(=O)NCC1CC1)N']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UyWT04pipWw76je4rPGzYsBdPJ2ppaaGPxQYNGvTsY7Hi4uLCwkL61uDBgzV6FcBW9bavvvoKAJYvX67Ro/zwww/0T5vilSfrVA7hpUuXAOCVV17R7+sTpojF4v79+wNAbGzss+9WVlbSeVjyXTEU52G1mQBZVlZGd4/r27ev5vbJYGsknDVrFgCos7uWkn7++Wc682b16tU68m9Y5RBu3LgRAN555x1N9EYv0VkNffv2lZeiefE8rGeLiMvV1NSMHz8eAOzt7bOzszXRW8UQHj5Mtm8n27dr4jhPkclkdA1AQUGBxg9GyKFDh4yMjADgk08+0cLhXkrlEL766qsAcFzD9Qv1iVQqpSPYqlWrwsPD6cAoP+GcOXPmDz/8oPwZZmNjI51EamVlxUhJ1RcQColEQrTwDzUvL4+eXmn8SE8kJyebm5uvXr36BX/ytEa1EBYWFnI4HHNzc3ZKbnZa8fHxHA5HPsPDxsYmKCgoNja2rq6uA62JxeJ58+YBgJmZ2d9//814b+UkErJhg6q7ZnVETEwMACxYsEDjR3ri8uXLAODi4qK1I76Aaivrjxw5Qgjx9/enozlSUmBgoI2NDSHk3XffPX/+fFlZWVxc3OLFixWX5CiPz+f/9ttvS5cuFQqFgYGBhw4dYrzDAHDiBGzZAqamj1fza1R6ejoAeGu4YsDatWtjYmKam5sBIDU1VQtHVJZKkaWT8bRw9ax/6KJ7BosFy2SyiIgIAODxePv27VO/wW3byPvvE0LI8uXk5EmydCk5eFD9VpVCtz3X0FUuVV5eTk/i6JYk9FRiz549mjui8gxemNCn1NXVpaSk8Hi8mTNnauTvgV5zcHAAgJKSEqYa5HA427Zt69mz50cffbR06dK6urrw8PAOtCOTQWYmxMeDqSkUFEBDAwDA1KlQUABPNtrRrEePHhUUFHTr1o2uDteQlJQUQsi4cePozTD6WNLHx0dzR1SeCiFMSkqSSCSTJk3q0aOH5jqkr2gIS0tLmW1WIBCYmJhEREREREQ0Nzd/+OGHSn6jSATp6ZCQAH/+CQ8fAgAsWgSLFsHevY8/sGwZsz19Lnpm6OXlRSspawg946V7y965c6ekpMTe3t7Z2VlzR1SeCiFMSEgAALqUHqmqZ8+ewOhIKBceHm5kZLRq1SqBQFBdXU3nhTzPgwetR48axMfD6dPQ3Pz4RScnCAwEY2NwdYXk5Kc+L5EAlwsGKvwzUZliPDQnLS0Nngx9NPY6MgyC8iFsbW2lK00whB1DQ8j4SEgtX77c0tJy8eLFW7Zsqaur27Fjh3zKG5WXl5eYmJiQkEBIcEZGOABwuTB6NAQEQGAguLkBhwPR0QAAAQHwww+Pv0sqhcWLQSiEuDh4stcj8xTjoSFCoTA7O9vAwMDDwwO0FXsVKHnteOrUKdDw1D799ueffwLAbE1u0piYmEg3aXzjjTckEklTU1NCQkJYWBhdBkU5OAyeN4/s2UPKyl7e4J07pEcPAkCmTiUNDRrpc11dHY/H4/P5bVbNMys5ORkAPDw86Jd0AmBmZqbmjqgSZUO4Zs0aAFi/fr1Ge6PH6J2AsWPHavQop0+fpkvv+vbta6pQBL1Pnz4rV65MSkoSiUQqNXjjBunViwAQd3eisLyBMfT0avz48cw3rSAyMhIA3n//ffLMbVJdoOzpaHx8POC5qBo0d02oaPLkyadPn543b56NjU1hYaGrq2tgYGBAQICXlxenQ1uDDBkCqang5weXL8OECZCcDArDKgO084RQ8fyzzW1SnaBMUnNycgDAzs5OF+b4dFJNTS1OTpLu3bUxZ1gsFufm5jJYc7GkhAwfTgBIv37k3j2mWiWEEBoMOgmE+v7775ld8tfS0kLXoJeVlZEnFQ90aiGeUn8MGhsbx40bZ2dn1+ZyHynPxMSgthZqa6G6GjT9iIfP5w8dOpTBBnv2hNOnYcYMuHIF5s0rPXiw3sVl0Mu/7TmkUml2dnZCQkJiYuKNGzdMTU1lMhl9Kzc3Nzw8nBBSX19P06K+7OzshoYGFxcXOzs70Mp9IJUpk9QzZ84AgI+Pj4b/Iui5IUMIAMnNZbsfHVVXR/z9qwcOHG5nZ5eVlaXqt9fW1h48eHDhwoXW1tbyf35mZmbwdAGYnTt3Mrvk75tvvgGAt956ixDS2NhoaGhoYGDQoKEbTR2iVAhv3boFAM7OzprujX6bPJkAkORktvuhBqFQOGPGDADo3r17WlqaMt9SUFAQExMTEBDA5/Pl2evfv394eHhycrJIJHq2AMwvv/xC9xd655131L8CUpyh1uY2qY5QKoR1dXX0j5ame6Pf3niDAJD9+9nuh3rEYnFwcDAdvp63ok0qldLqoKNHj5YHj8fjeXl5RUVF5eXlKX5YJpN98MEH8HQBmISEBGNjYwBYtGiRmrcx6VwlWiRe8Tap7lD2EQW9361Tg3ins3YtASBbtrDdD7W1trYuW7aMDl+/K2yGJxQK4+Pjw8LC6L97+QlnQEBAbGzsi3eqerYAzJkzZ+jjltmzZ6v6ZEUuPz8fAOzt7emXdAXCoUOHOtaahigbQroUVXNVFbqCrVsJAHnvPbb7wQSZTLZ27Vo6fH311Vf0hFNxgVu/fv3CwsLi4+PFYrGSbcon+sivBi9dukQnKk+ZMqXDA0Bpaen58+fJM7dJdYeyIaS3ks+dO6fR3ui3n38mAESLK1c1jp7d0UpwNJCjR4+OjIzs8GQUeQEY+dVgbm4unfHj4+Oj5s5ftDySjizkVaTs80rGV+J0QfQcTZ9+hZs3b3Zycmpubj5z5kxgYODMmTPVXGGzcOFCCwuLkJCQHTt21NfX79mzZ+jQoWlpab6+vqmpqVOmTDl+/DitRtMB9OGErizkVaRkWFevXg0A0dHRGv2ToN+qqsiRI+TaNbb7ofPkk+9mz55NC6kUFhbSPdIGDx5cXFysaoPNzc0nTpyg+xbq4BZGyj58x5FQffv3w7lzMGIErFgB0dGQlgYAsGAB293SPZMnTz558qS1tfWRI0fmzJnT1NTUp0+fc+fOjRw58tatWx999JGS7VRVVf3++++LFy+2t7efPn36nTt3zM3Nd+7cWVFRodH+q0q1EGpoJU7XIV+6jl7Mw8Pj5MmTdnZ2SUlJM2bMqKurs7e3P3PmTFhY2I4dO178vXl5eVu2bPH29ra3tw8ODj5w4EBdXd3IkSPfffddW1vb69evT5gw4cGDB9r5QZTBIYQo87mkpCR/f/9p06adOHFC033SV9HR0KcPPHgAN27A4MGQkQGOjlBQAEeOsN0zXXX79m0/P7/i4mI3N7cXXw1KpdLz588nJiYeOXKEzi0BAAMDg7FjxwYFBc2ZM6dPnz4AUFZWNn369GvXrvXt2zc5OZmeoLJPydPWrKwsABg+fLgmz4313LZt5OZNsmIFWb6ctULXnY58D/p2rwarqqpoCXP5HVp4WUVJeQHlnj17XtONC3RlQ0hPRGndPtQxNISJicTfH0OogtLSUlpy2snJiU58kZcwp7PbKPlUuJfOsNFmAWVlKBtCqVRqYGDA4XCUf/aKFEmlxNWVLFlClNjZErVVVVU1duxYGhtaH5Hi8/l+fn7fffedqvXzxWIx3ejWzMwsme3pvCrUHaX3ZjpwgxgRQtLTCQDp35/tfnRajY2Nfn5+U6dOBYAePXrQE051Ht+3trYuXboUAIyMjNidyKZCCBkvX9ulCAT6M2eNLc3NzVVVVWlpaS/eZ0558gLKBgYGjBRQlhMKhcrPO1dhkS4+KlRHfDwAAJYHUYeRkZG1tTWDFUppAeWoqCg6Kn777bdqNlheXr5///7g4GB7e3taG00ZKpTZwEeFHXb3Lty8Cd27gw5OmUICgQAA1q9fHxERIRaL6cbayiOEXL16lRYKoDvnAgCXy7127dr06dOVaUGFEGqnVJFeOnwYACAgABRu5iEdIhAILC0t33nnnQ8//LCqqurFBZSp5ubmtLS0hISEQ4cOyR/9m5iYeHl5BQQEBAUFOSpdEgtHQm1ISADAc1HdtmLFCktLyyVLlmzZsqW+vv77779vt6JSZWXlsWPHEhMTjx8/3vBk9pOdnd306dMDAwP9/f1pwQ7VKH+tee7cuUmTJk2dOrUD16ldWVUVMTAghoakpobtrqCXSUhIkBdQVryzkpubGxUV5eXlpZhM+ebKapbQUyGEQqHQxsYGAI4dO6bOIbuagwdLDQ0lfn5s9wMp5+zZs3TfyICAgFOnToWHh9Mpb5SJiYmvr290dDSDz+pU25/w66+/BoARI0ZgAVLlzZ8/v1s3y927k9juCFLWhQsXrKysFO/B2trahoaGxsXF1dfXM3441UIoEonoX4VffvmF8a7opebmZro0TtUpHYhdX331FQDY2Nhs2rTp8uXLGq3ZrFoxX2NjY1rR4OOPP5ZIJCpfgHY9p0+fbmhoGDVqlJOTE9t9QSooKioCgPfee++TTz4ZM2ZMxzYRUJLKFbWXLFkydOjQgoKCn376SRMd0jN0U8dZs2ax3RGkGm3WwlB2PaGiv/76a+7cuXZ2dnfv3qXnWqhdhJA+ffo8ePAgMzNTsQIn0nH19fXW1tY8Hq+mpkZxcysN6cjeEnPmzBk3blx5ebn603z029WrVx88ePDKK6/Qabeos8jIyJBKpe7u7lpIIHQshABApxRs3bq1qqqK0f7oFbqfHADQCrnsdgYpT8t12ToYwgkTJkyfPr2urm7Lli3Mdkif0BA+fPgwJCTEw8ND+Rm9iF00hNrbT7vD91Wzs7O5XK6xsXFRURFT92r1yZkzZ4YMGWJra7t79275NEJfX98rV66w3TX0IhKJxNTUlMPhVGpia+L2dDyEhJAFCxYAwLJly5jqjd4oLCy0t7cHgL179xJChEJhVFQUrYPC4XCCgoJomQakg+jG5sOGDdPaEdUKYX5+Pp/P5/F4N27cYKpDeqCpqWnMmDEAYGBgsGzZsvLycvp6ZWXl2rVr6WZDfD7/pw0biI5tioAIIfQKa8WKFVo7olohJISsWLECAObPn89Ib/SATCYLCQkBABsbG/qE18rKasuWLU1NTfQDxcXFYWFhPB6vYswYYmZGBAKi3hYLiFn0oe6BAwe0dkR1Q/jo0SN6Ak03vkF0RlG3bt1yc3OvX78eEBBArwZfeeWVmJgY+cT8e3l5JDCQABAAYmtLtm8nWEFLB8hkMlrd9P79+1o7qLohJITQsuQTJ05Uv6nO7s8//+RwOFwuNzExUf5icnKy/Em9i4tLXFzcfycinj9PJk58HMU+fUhMDGGoegrqmLy8PPoXU5sHZSCENTU1dBdy1kvHsSsrK4su6Pzmm2/avCWTyX799dcBAwbQKP60cCFR3GQuOZm8+urjKA4dSp7sGo2078cffwSA119/XZsHZSCE5Mk2q6NHj9boZHNdVlpaSteXLF68+HmfkUgkMTExfXv1au7fnwAQX1+SlfX4vdZWsmcP6d2bABALC/LCTW2R5oSGhgLA999/r82DMhNCkUjUq1cv6KpLnCQSycSJEwFg/PjxdCuvF2hpaCCffEIsLAgA4XLJ4sVEfvkhEpGtW8kzAynSGlpZWMvl8ZkJISEkJibGw8OjR48ehw8fZqrNzuLtt98GAEdHx4cPHyr7PZWVRCAgxsYEgPD5JCyMlJY+fquoiISEkNBQkpCgoQ6jdtF6TZaWlkzVNVUSYyGUyWTvvfceAHC53C1btjDVrO6j1QZMTEwuX76s8jfn55OFCwmXSwCIpSX57DMiEpENG8jVq0QmIwEBGugveq5ff/0VAPz9/bV8XMZCSAiRyWRRUVG0Es6CBQvkT8b02IkTJ+gWHQcPHux4K7m5JCiIAJB+/YhYTN58k1RUEELIrFlM9RMpg25H/fnnn2v5uEyGkDp69CitkzNq1Cj9nlZ669YtOhNt8+bNDDR3+jQ5cYIQQjZtIpmZRCYjs2cz0CxS2siRIwEgJSVFy8dlPoSEkJycHHqB6+joePHiRU0cgnXV1dWDBg0CgBxC/Y8AABMwSURBVLlz5zJ8T7i4mISEkMWLyfHjTDaLXqilpWXChAkWFhbaP4PTSAgJIeXl5RMmTACAqVPX/Pqrhg7CmtbWVn9/fwAYOXJkI+51pkckEon2D6qpEBJCxGKxQPCNoaGEwyEbNhB9KpL47rvv0tmh+fn5bPcFdXoaDCEVE0MMDQkA8fcn7e1e3Mm0tLSsX78eAIyMjNLS0tjuDtIHGg8hIeTcOWJrSwDI8OGkU48cycnJw4cPBwAHB4eoqCi2u4P0RAfLW6hkwgTIyABXV7h+Hdzd4cwZLRyTYSdPnnR3d/fz87t+/bqpqWlJSQldMYiQ+rQRQgBwdoaMDJgxA6qqYOZMKCrSzmEZcPnyZT8/Pz8/v8zMTFtb26ioqBkzZgBATU0N211DekKFrdHUZGkJiYnw4YdgaQkKG2zortu3b2/cuPGPP/4ghFhZWQkEgvDwcBMTk7CwMACorq5mu4NIT2gvhADw3XcwZw54e8OCBeDpCcXF8PXXsGIF/PijNnvxcg8fPvz000/37NnT2tpqamr67rvvCgQCKysrAGhoaKD/B0dCxBQtnY62q6AA6C6Lra0s9uIp1dXVH3300cCBA3ft2sXhcMLCwu7cuRMVFWVlZSWRSHbt2jVgwIDKykrAECLmaHUkBIBvv4U//gCRCABg0SLYuxcA4KOP4K+/ICAAAgNh0iQw0HanAACampq+++67qKio2tpaWhDt888/HzhwIAC0trbu3r373//+98OHDwHg/v37gCFEDNLmrdht20hqKiGEhISQbdvIzZtkxQqyfDlxd3+8rByA2NmRt94ihw8ToVBLvaJrbelm4ADg6+ubmZlJ35LJZHFxcXR6GgCMGTMmPj4+Li4OAIKCgrTUP6TvWA5hYiLx9yctLeTUKRIRQeiKc/o/ExMSGEj27BGXlJRoqD80Y3S4AwBaJFv+7vNqwyQnJ9OsaqhXqKvRagiVce8eiY4mXl6PF9mNG1cEAK6urpGRkZmZmepMlZZKpY8ePcrKyjp69OiePXs2bNgwbNgwmrEhQ4YcOnRI3vilS5emTp1K32pTJY0QkpmZCQBubm7q/qgIEUII6cjWaNpRWgoJCZCd/fvevUtE9CISoH///rNmzQoMDJwwYYLBM9eOYrG4qqqqpKTk0aNH9L81NTXyL4uKilqfvgU0evTosrKyjRs3vvnmm7S1W7dubdq0iT6WsLa2/vDDD+ljCcXvys/PHzBgQL9+/fLz8zX5C0Bdhe6GUK6pqSk5OTk+Pj4xMbG8vJy+aGVlNXnyZEdHx5qamocPH5aVlZWUlNTW1r64KXt7ezs7u1deecXe3t7BwcHf39/d3Z2WxC4vL1+/fn1sbKxUKjUzM1uzZs26devocsE2amtrraysunfvjvdmECM6QQjlZDJZVlZWQkJCYmLilStXrKys2sSAz+f36NHDysrK0dHRwcGhzX979+5taGj4vMYrKysHDBggEomWLl26efNm+X2adrvB5/MJIRKJhMfjMfkToi6pM4VQ0d27d318fEpLSyMjI729vR0cHOzt7W1sbNRp86+//hoxYoS8OugLWFtb19TUVFVV0YKrCKmDzYf16nB2dg4KCgKA1tZWX19fV1fXb7/9duzYsU1NTR1uc86cOcokEABw0gxiUGcNIQAEBgYCQEJCAgBwOJwTJ05cunRJOxtx0gEQQ4gY0YlDOGnSJEtLy5ycHDqFRTGTmoYjIWJQJw6hoaGhn58fABw9ehSehJDuxKLpQ2MIEYM6cQjh6dHv1VdfdXJyKikpuXLliqaPS0OIq5kQIzp3CF977TUej3f27NmGhgb6JWjljBRHQsSgzh3CHj16jBs3TiwW0/mcWrssxBAiBnXuEMLTwZs0aZKFhUV2djbd2YMpQqEwODj4xo0b8lf4fD5gCBFD9CSER48elUqlRkZGfn5+hJDExESm2pdIJHPnzv39999DQ0PpLZ/6+vq9e/cOGDDgjTfeYOooqCvr9CEcMmTIwIEDKyoqLl26BEyfkUql0tDQ0L///tvW1vaXX37hcDgikWj27Nk5OTkcDmfw4MGMHAV1dayt32BOREQEAKxfv54QUl5ezuPxjIyM6uvr1WxWJpMtW7YMACwtLa9cuUIIkUgk9N5Pr169CgoK1O85QkQH1xN2AJ0lM2zYMPrluHHjAOCvv/5Ss9m1a9cCgKmpaWpqKiFEKpUuWLAAAGxtbW/cuKFupxF6Qh9CKJFI6Jqju3fvEkK++OILAHjzzTfVaXPjxo0AwOfzk5KSCCEymYxWOrS0tJQXv0CIEfoQQkJISEgIAGzfvp0Qcv36dQCws7OTdnQPmu3btwMAj8eLi4ujr6xbt46OitrfvA7pPT0J4c8//wwKdV9GjBgxf/786urqDjS1b98+DofD4XB2795NX/nkk0/oqHjs2DHGeozQE3oSwqqqKgMDA0NDw5qaGnXa+fPPP+k63W+++Ya+8t1337UZFRFilp6EkBBC9yRVJyp///23kZERAHzxxRf0lf3793O5XA6H89NPPzHUTYTa0p8Qbt26FQDoI/UOSE9PNzMzA4CIiAj6yl9//UWrP3399dfMdROhtvQnhLdu3QIACwuLpUuX7tmz5/bt2yp9+4ULF6ytrZcuXSovLkpHxc8++0wz/UXosc5aY6ZdZ86cmTZtmryuoZ2dnYeHh7e3t5eXl4eHB53w+QL37t3r168fl8s9f/78tGnTGhsb16xZEx0drfmOoy5Nr0IolUqzs7PTnigtLZW/ZWZm5unp6e3t7e3t7enpaW5u/rxGrl27Nnny5Jqamv/93//ds2cPh8PRSt9R16VXIWzj0aNH6enpaWlp6enpV69elf+kPB7PxcWFjpCTJk3qo7Bb4p07d3x8fMrKyubOnRsXF4cVDZEW6HMIFdXX11+6dOnkyZNpaWmZmZlisVj+loODAw3kwIEDV61aVVhYOG3atPj4eHpNiJCmdZUQKmpqarp69ap8kJQvC+RwOISQSZMmHTt2rE3pe4Q0pyuGUJFUKr127VpqampaWtqJEycaGhoyMzPlmzEhpAWdfj2hmng8npub25o1a37//fc5c+YAwOnTp9nuFOpaunoIFWmzcilCcl39dFRRY2OjjY1Na2traWmpmttatKO4GNatAz4fgoMhIIDhxlFnhiPhf5mbm0+cOFEqlR4/fpz51mNiQCCA2FiIiWG+cdSZYQifosEz0pIS6N0bOBzg4u8cPQX/QTxl1qxZAHD8+HGJRMJYo3V1AAC9ekFhIRACOAUHPQ1D+JQ+ffoMHz68vr4+JSWFmRZbW6F3b3BxgYULYetWeP11WLmSmZaRvsAQtjVr1ixXV/cLF6TMNJedDQ0NAACDBsHBg1BQAEuWQE4OM40jvYAhbCsg4N83blzau3c6M82lpQEAeHsDAAiFkJUFVVXQvz8zjSO9gCFsy8OD07Mn5OdDXh4TzSmG8MIFaGmBUaPg+Ws4UBeEIWyLywV/fwAAZm6RZmQAPAmhYiARegJD2I7AQABGQnjnDpSUgL09DBwIgCFE7cMQtsPPD4yN4cIFKCtTryHF1LW2wsWLAADjx6vbv/aIRCLFRcyoE8EQtsPMDCZPBpkMkpLUaqe6723h6omt83wBntwmHTQIevZkpJOKNm3a9MEHHzg4ODg6OgYHB2/fvp1unsH4gZAm4NzR9u3cCatWwdy58OefHW8kL8+lufmfIUMum5qOqcuL4fx80KSHu+EHXzHXTQCA//znP+vWrePxeMbGxkKhUP66nZ3d+PHjfXx8vLy8Ro8eTSvHIR2EIWzfw4fQuzeYmUFFBRgbd6SF1taKa9fsuVzTkSNrORyD/Pz5NTV/Ojnt6dFjKYP93Ldv35tvvgkAu3fvXrx48a1bt+hi5ZSUlMLCQvnHTE1NR40aRQsIeHt7052GkY7AED7XsmXg4gIrV3bwgUJt7aF79+Z16+Y7cGAyAOTkOLa0lAwb9o+R0UCmenjo0KHg4GCpVLpt2za6P5wiZUrsTJw4sW/fvkz1B3UQG3UWu4Ti4vczM+HRo82EkObmfzIz4do1ewbbP3HiBK2C8+WXX770wxUVFYcPH167dq2np6ehoaHiPwBnZ+clS5YcOXKEwb4hlWAIn2vbNvL++4QQsnx5R7795k2PzEyorz9JCKms3JOZCffuzWOqb2lpabReON0aVSVCoTA1NTUqKiogIEB+XhoeHs5U35Cq8GL9RQoKHk/8DAmBpibw9gZvbxgzBl5ah00ma2pqyuZwDMzMxgJAY2M6AJibM/OE8PLlyzNnzhQKhStXrqSbMarE1NSU1l8VCARSqTQnJyctLc3d3Z2RvqEOwBC+yKJFsHfv42cVDQ2QmAgAYGAAr74KXl7g7Q1TpkCPHu18o1h8j8ez5PP7crnmANDYmAoMhTA3N3fmzJkNDQ0LFy78/vvv1WyNx+ONGjVq1KhR6ncMdRg+J2zHnTswfjxUVYGrK9y8CVwu3LoFcXEQHg6jR4NMBleuwLffQnAwvP56RW7ugMLC5VVV+8XiAnkLJibDX321bODAJACQyZoIkXK5ZiYmI9Xs2L1796ZPn15VVTVr1qx9+/ZxcX2wXsC7o20VFoKPDxQXg6cn7N0L9+7BDz/A0aP//UBNDWRkQFoapKXB5MlJs2f7y98yNHQwN/c2N/eig155+Q4ez9zYeLCt7SqptIbHU+vBwMOHD318fAoKCqZMmXL06FHjjj05QboHQ/iUigqYMAFu3YLx4+Hvv8HM7CWfJ6RVJLrW2JjW2Jje0HC6tbVK/paDw3ozMy9Ly9eam28ZG7sAqLWgvrKycuLEiTdu3Bg7dmxycrKFhYU6rSGdgiH8r7o6mDwZsrJg5Eg4cwa6d1e1AZlIdKOxMbWxMb25+aaLS0pZ2X/E4gJLS/9HjyK5XDM6QlpYTDEwaO868vnq6+unTp2amZk5fPjws2fPWltbq9ozpMswhI81NcH06ZCWBoMGQUoK2Nur22BjY6q5uTcA586d6fX1JwFkT97hmpgMNTf3MTf3Mjf34fN7v7gdkUg0Y8aMlJQUZ2fnlJQUBwcHdXuGdAyGEABAIoFZs+DECejTB1JTQWGbpo4Qi+/w+U719Sdran7j8awMDGzs7N5pbExvbExvbExraroskzXLP3zt2munT3f38vLy8fFxdXVtc6+lpaXlf/7nf44dO9arV6/U1FQnJye1eoZ0EoYQWlpaQkNFv/3WzdERUlMZKD2Rk+MoldYNG/aPoeErYnGBgYENj/ffS7g2l5Hbtg2NjX1cVMrCwmLs2LF0eqe3t7ehoeHChQt/++03W1vbc+fODRkyRN2eIZ3U1UNICHnrrbfOn78mkx0/dMh26FB1GxSL7+bmDjQwsH311TIAzp070xoaTjs7H+/Wzbe9j8tu3755+nRKenp6ampqUVGR/A1jY2NbW9vi4uLu3bufOXNm5Eh1H28gndWlH9YTQlatWrV3795u3bqdOlU8dKit+m02NqYBgIXFBAAOIa1C4QVCpCYmw57zca6Ly1AXl6ErV64EgJKSErrHcHp6elZWllgsHjdu3JYtWzCB+q1Lh3D9+vU//vijiYnJkSNHxoxxY6RNGkIzMy8AEImypdIGY+NBhoZKLeR1cHAICgoKCgoCgNra2vz8/FGjRuF+3Xqv64bwyy+/3LJli6GhYVxc3KRJk5hqloaQPqxX/P+q6t69u5sbM38XkI7rovOedu7c+a9//YvL5e7fvz+AuT2SWlsrmpv/4XLNTE1HgnohRF1HVwzhL7/8snr1ag6Hs3PnzgULFjDYcmNjKgAxNx/H4RgCQGNjBmAI0ct0uRBWVFSsWLFCJpN99dVXYWFhzDZu8X/XXL+b0LP8NQAQi++0tJQYGtozuJQe6aUud01oa2t75MiR8+fPf/DBB4w3zvvjuMmlS7BoIwAYJlwc/tv45mB3GMH4cZBe6erPCZnU1ARWViCTQXU1WFjAsmWwezds2wbPVH9BSFGXOx3VoAsXQCKBUaOALnFITQXAetvo5TCEzFGst11RAXfugJkZ4HN29DIYQubQEHp5AQCkpgIhMG4cYMld9DIYQoZIpXDhAsCTrSbS0wHwXBQpBf9OM6SsDFxcQCgEut4PLwiR0jCEDHF0hMuXobUVAIAQsLYGS0vw9GS7W6gTwBAyp7gY1q0DPh+Cg+H4cZBKgcdju0+oE8DnhMz5+GOYNw9GjoRZsxja5hd1CXhjhjklJdC7N3A4gOVAkSrwnwtzevWCwkIgBHAFIFIFno4y58ED+OADMDKCN96A6dPZ7g3qNDCECLEMT0cRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYhmGECGWYQgRYtn/A4Vf39favs1IAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+fn40mYqnhWpWmnshQkgIKSwsTEtLS09PT09PP3v2LAAEBAQw6YxiPCMSiZh0oDWlpcXXrkUlJ9Pf1Y0bN6RSafOv6+jolJSU2NnZ0Q0EWk9p8Nn8Q5lMxtk2Li5u2h1OTU3DpUuXtm/fPn/+/FGjRpmYmCj91CKRyNfXt/XKhXyor6+nSy6uX78u8KWfobGRZGaSQ4dIaCjx8SHm5gSgfPDg5r+oXr16+fj4hIaGHjp0KDMzk45F/+///o9114WgtG0iJycHABTbJk6ePAkA/fr1U/9CWnInLCmBlJT/VeutqtK9c2dk828wNzd3dnYeNGiQs7OziYnJ22+/HRsbu2LFivDwcCH7eezYsYcPHw4dOrR///5CXvcply/Dxo2Qng7Xr8PTNzowNTXq3ftDd3f6u3JyclKqI+7n57dt27bTp0/TmXpBuy04kUgUHh5+8+ZN+h6ePi0MDAykvxOlp4VqUT/HgomMJFOmEELIW2+RigqyfTuZP5+MGkW6dHmqVC8A0dMj7u5es2bNWrdu3ZkzZ1o+DPztt99oOYZt27YJ+SNMmjQJmC96PnHif7+pXr2Ijw8JDSWHDpHMTPL8jR0VFRW//vprWFgY/Yv08vLq6Ed8c61fv34AEB8fT56seQSAHFquVj0aFkIvL5KTQ956ixQVPZU6U1Pi7k5CQsj27SQhgdTX/31r33//PQBIJBK6G0UAFRUVnTp1EovFpaWlwlzxf+7dI1OnknnzyOrVpLycREaSK1dIbe3zvr2pqenmzZtxcXGhoaGBgYGOjo6Kd7A+Pj7du3cHgMWLFwv5E7B18eJFaLZtgt4G3dzcOGlcw4ajwcGwZQsAgJUVvPsuODiAiwu4uEArj9xLS0vT09PpTIOZmdmGDRvo52fPnn3t2rW1a9dOmTLl999/F2B8GB0d3dDQ4OnpaW5uzve1lO3YAXPnwtixQKej5s5V/obKSnrcDaSnn2xo8I+Jqa+vb/51IyOjl19+2cXFZfTo0R9//PG4cePWr19vZ2dHaxxrPZq6mTNn0m0T9vb2M2bMoDsMOcBJlIURGUkSEsjSpWTcuOd+T0MDSU0lu3eTL77IGzt2LH3NVrC0tGz+zTKZzM/PDwBsbW3Lysr47v+oUaOA1aJnpTr/hJA7d0hcHAkNJYGBxNGRiESKcUW+uzsA9OrVa+zYsSEhIbt3787MzFQafP7www8AIBaLT58+zeDHEVZjYyM98Ien+kCaFEJ6MERhITE0fOrz+flkzRoycyZxdiYSyV9/SwMG1NLgde3a9bXXXvvwww8jIyOTkpKU2qyrq6Oz7aNGjeJ1QwNdZGhoaKjOIkPVKdX5X7xY+W20kREZNoy8+y7ZtKkuMfHhw4d/2yQ9c8rExIQ+yNZix44ds7OzmzZtGk/ta1II//EPMmoUuXVL+fMnT/7vb0lXlzg4kMBAEhYmjYuLKyws/NtmS0pKLC0tAeDtt9/mo9sUXfT81ltv8XeJ1pSVkWnTyLx5hFYi+uEHYmNDJk4kn3xCDh0iN26Q9j+tkcvl9Fmrvb19WZlGbshqI7pxOTw8nKf2NSaEqakEgHTvThoblb9UVkY++IDs2EEuX25lrqH1xlPphPsXX3yhflefiS65+OWXX3hqn4m6ujoPj9fc3A64uxPN3Bf596qrqw0NDRXbJvigMSFcsoQAkA8+4Kt9Xg8VTElJAYDu3bs3tnwJ0XClpcTKigCQt95q5QGHBqPvfl9//XX+LqEZIZTJSJ8+BIDwetoff4cKfvTRR6C9x1FkZf31qPbTT1l3hQd0s0hkZCR/l9CMEJ47RwCInR3vr7WKQwXb8mayjWQymYWFBQBcvnyZqzY7mhMniFhMRCKybx/rrnCqpKREV1dXT0+vvLycv6toRgjnzCEARIBCz1KplL7yOTo6tmWGsHV1dXVJSUn0Nqj1teIjIggA0dcnGrhD67nWr18PAP7+/rxeRQNCWF9PunYlAESYNc9VVVV0EsXT07O9+8Tu3Llz5syZiIiIoKAgV1dXxUFF1tbWdnZ29W1ZyKPJFiwgAKRbN8Kifh0vBg8eDAAxMTG8XkUDQnjoEAEgw4YJd8WCggJa9qL1rav19fXJyck7d+5cuHDh6NGjzczMlBZCiMViJyenyZMn09ZmzJih3TfDpibi40MAyMCBpLKSdW/Udu3aNWhWZI0/GrBsjZ4WIeQmUhsbm6NHj77xxhsbN250cHD44IMP6OcrKyvp9taUlJTs7OzMzEylswe6du3q5OTk5OTk6Ojo6uo6ePBgQ0NDAMjOznZzc4uKinJwcAgNDRXuJxGWri5ERYG7O/TsyborXNi3bx8ABAQE8F4Rj9eIq6+8vHzkSF9398PCH2S0b98+kUgkFov9/PzGjBnzvBvd9OnTw8PDf/nll+Li4lZaO3HihFgsFolEWl+rs7SU7xOuhKAosnb+/Hm+r9XRQ7h161ZgV+l5/vz5pqamitR16dLF3d09ODg4IiIiISGhtp0rA+jacX19/YsXL/LU4Y6j+b4z+uGPPxJCyPjxLHvVdgkJCQBgaWkpwM7vjj4cFbgsmlwup0W1KBMTk8rKyuHDh69atcrZ2ZlupVPZhx9+mJOTs2nTpokTJ/7xxx/29vZq97dDq6mB3FzWnVCVUpE1fvGdcnVwUlm1Xd54441JkyYVFRURQuRyOa0tmZCQwFX7Uql0woQJAODqOrSyUpsnaSIjSWwsWbTof3dCHx+ycCGxtWXdszZQbJtIT08X4HId+k5Iy6L5+voKU0mhqKgoPj7ewMCAvv1LSEgoLCy0tramVV85IRaLDx48OHFi4P37X06dKvr5Z77PJmXppZdAIoGysr8+fOcd8PWF69eZ9qltTpw48eDBAxcXF1oonW8duvivwJWe9+/fTwjx8/OjmeepLJqJicnu3afKy11Pn4b33+ew4Q7k0SM4fBhycmDBArh4kXVv2k/o2tAC3G1Vo1j0rGZl1bZrvtFBqeor55KSiKEhASDffMNH84z98AMBIHyueeaRosjarZa75vjRce+E9NVo+vTpalZWbaOrV6/S6u5jx44FgJ9//rm8vHzw4ME0mZwbMgR27waRCBYvhp9+4uMKLAn/aJdD9Gz61157je4yFUAHDaFcLo+OjoYWQ4K6ujqerqjIPD0fQoABSUAAfPopyOUwcyakpfF3HaGVlkJ8POjpgb8/666ohME5JcLccNvr7t27o0aN6tq1q2IsKpVK582b5+HhwccaIqWNDooBSevP39Unl5OgIAJAevcmt29z1mx1dTVnbbXf+vUEgPC85pkvSmfTC6ODhlAul9OJqffee49+pqSkpHfv3gAwZ84czi935swZALC3t6drOyMjIwFg7NixnF+opcePyahRBICofNqfVCrNzMw8dOhQaGioj49Pr169Bg4cuGTJkoqKCk572laDBxMAwvOaZ77QYtABAQFCXrSDhpAQkpycTBdefv311/QzV69epUe4cF7t41//+hcA/Pe//6Ufvv766wCwa9cubq/yPPfvk3feaceK57t3yenTZOvWgpkzZzo7O7c8X42udRwzZozwG/mvXSMApEuXNpV+7QgqKioSEhIiIiKCg4Pd3d3p0bGbN28Wsg8dN4SEkMOHD+vo6Ojo6MTGxtLPHD9+nBahOHDgAFdXqa+v79KlCwDcuHGDEFJcXEwHJOrvJ+SEVPrUgRG9ev1V0srRsUaROqUTI4qLi+nintmzZwvc2//8hwCQd98V+LJt1djY+Oeff+7Zs2fJkiXjxo1reUQsXR9jb29PDz8TRocOISFk9erVAGBoaHjlyhX6ma+++goADAwM/vjjD04uQc/WGTFiBP2QDkimTp3KSeMqO3aMEEL27iV6esrVCbt0IR4eZNEi6Y4dOy5fvvzMUxZTUlLoqGHt2rWC9VkuJ337EgDC/5rntlK60enr6yulzsTExNXVNSgoiK4Hvn//Pi2BydPswzN19BASQmbNmgUAvXv3vv1k7uL999+nL/+cPMn55z//CQAbN26kH9L3onFxceq33HZKy51PnyYzZpCtW/86NkLpwIg2riiOiYmh44ijR4/y2PVmLl26ZWnZaGmpQv1Ebii9Pe7bt2/LmUilUUPLHZ7ClMBsTgNC2NjYOHr0aAAYPHgwfdVvbGykFcidnJzUHDSWl5fr6emJxeK7d+8SQjIzMwHAzMysoaGBm963TfNjNqitWwkhpLGRqHOacFhYGB01CFPeZt68eSKRzmef7RLgWi0VFha2rMmvdKNr4yLkq1ev0lVTX375Jd/dJhoRQkLIgwcP6Jk4/v7+dGtJVVWVk5MTAEyYMEGd44E2b94MAF5eXvTDjz/+GADmz5/PTb/bTGm5M4eCg4M5HDW0QuBFzy1NmDChU6dOFhYWAQEBn332WWxsbH5+fnsbUbz48loCU4lmhJAQcv36dbq179///jf9TH5+Pj1qQp0zK93c3ODJqlGZTEbHIcLv92vLMRuqUYwaXnnlFV43o/z0008A4OLiwt8lWkFPk5dIJOpMqFy8eNHa2lox+7B27Vo6juC8BKYSjQkhedahggkJCXQ6XuU55cTExAULFtBR7q+//goA1tbWwleCoSFsecwGJ8rLyx0cHIDnQwWnTJkCAGvWrOGp/dZ9++23ADBp0iR1Gnn33XcBwMLCQrFIQ1ECk+5u44kmhZA861DB3bt3A4Curu7x48fVbHzu3LkA8Mknn6jdzQ7n5s2bvB4qKPyiZyV0SvPQoUPqNNLY2DhmzBg6alDMPtC1xOrPPrRCw0JICFm6dCmdO1Ec+75ixQr6FlyddyOK0+QzMzM56mm78VoD4sKFC3TUsGXLFs4bpy+OY8aM4bzltsjNzRWJRIrT5NPT08+dO6daWQrFqMHb25uOGtQpgdlGmhfClocKyuXywMBAKysrdbYdHTlyBACGDBnCXU/bje9CLPRYBYlEwvmhgvRt586dO7ltto1oATvFesa3334b1FhWlZeXR2eYli5dSj+Tn59PH+svXLiQmx4/TfNCSJ51qGBdXd3d9tdjk0qlWVlZBw4cWLZsGV2Yqlgix4QANSCWLVsGXB8qyGTRc3P03nXu3DnSYv2TahSjhq30SVGz2YdNmzZx0+lmNDKERNUnqg8fPkxISNi+fXtISIi7uztdm6qwbt06FZLMIQFKksnl8mnTptFxxL1799Rp6sGDB+fOnfvmm2/oC6K3tzdXnWyXS5cuQbPT5Gk1BsX6J5Xt2rWLjhrOnDlDP3Pw4EGRSKSrq3uMrmbijqaGkLThUMGmpqbs7OyDBw8uX77c29u7Za00kUhka2vr6+u7atWqI0eOMF8sqhTCn38ma9eSFSs4vkpdXd2IESMAwN3dve0rs5qamm7evBkXF6dYjNK86oeZmZm9vT2TLVS0NPOSJUvohz4+Ps3XP6mDPjQ2MTFRTBN88sknAGBsbMzts1ANDiFp8US19RsdAOjp6Tk6OtL1E2fOnLl//z7rn6A1dK2MGg9Bn6u0tNTKygoApk6d+rznMeXl5fHx8REREXPmzHF1dW256rJz584jR4587733vv76azog5G/q4nmkUmnPnj0BIDU1lbRY/6SmlqMGuVxON/va2NhwOGjS7BCSJ+fm6Onp0Td1SmxtbSdNmrRy5crDhw/n5uYKUMiVQzIZ+fxzwtOB8JmZmfS902effUaevtEFBgY6Ojq2LG9FV13++9//3r17d2ZmZvNfpmLqQuAzGI8fPw4AAwcOpB/S9U8cDozr6uqGDx/efPahvr5+5MiRdA6vvdWfn0fjQ0gIcXFxoUNNzbrR/a1168jy5SQykvD0gF2xL8zBweGZN7oRI0YEBwdv3rw5ISGhqqqq9dYSExPp1AUnQ8E2mj59OjRb4UnXP3G70Ewx+6AYNdy/f9/Ozg4AAgMDOVnXofEhVCxZPHbsmGbd6DqCWbNmKfbUtXKja6Po6Giepi6eqaampnPnzorT5PkrFa0YNXz++ef0MxkZGSYmJgCwfv169dvX+HsSSRgAAAxaSURBVBDGxcUBgJOTE+uOaCRPT086q8HVpNTKlSvp1EVaWhonDbZiz549APDqq6/SDz/77DMACAoK4uNacXFxurq6y5cvV3xm165dJiYmGzZsUL9xfkP4/fffz549m8Ojp1uaOnUqAHz11Vf8XUJb0UXPenp6HO4iV0xdWFtb8/28Z/z48QCwfft2+qGjoyMAnDx5kqfLZWVlNf+QZn7WrFnqt8xvCOm5C/wddFpdXW1oaKgYkKB2iYiIALUXPbfEx9RFS0qvIElJSQDQo0cPwaZnBwwYAACnTp1Svyl+6466uLgAQHp6Ok/tx8TE1NXVvfbaa/TkFtQuPBXY1NfXj4uLs7OzS05O/te//kUI4bZ9KioqqqmpycvLixZKpz/LjBkzhCkVnZycfP369R49etAF3+pSP8etiIqKAgBfX1+e2qcr3CMjI3lqX4spLXrmXHZ2Nl0Qv3LlSj7aHzJkCAAcPnyYENLU1GRubg4ASUlJfFyrpUWLFgHAokWLOGmN3xBmZWUBgC0/SyFLSkrYLlnUaKtWrQJ+irgqnDx5kh5OvGfPHm5bzsnJAQDFK8ipU6cAoF+/ftxe5Xk4zzy/w1H6AKqwsLC6uprzxqOiomQymbe3N33FRe1Ca8zxWux9/PjxdAH03Llzz58/z2HLe/fuBYDAwEADAwMAyMvLMzAwCAoK4vASrTh79uzdu3f79etH78Yc4CTKrRg8eDAAJCYmct7yK6+8AgCClRLTJkqLnnkVEhICAC+99FJOTo76rclkspycHLrmLj4+XvH5qqoqwQZENO10pREneA/h7NmzQY3yE8+TnZ0NAKampoIVh9QmhaGhXw4fvvJJtR5eyWSyiRMnAsCAAQNUqMxfVVWVnJy8e/duuh6YLtnv2bOnsbExryUnnqe2ttbY2BgAOHlNoXifSnJ2dgYeJkj37dsHAIGBgXSpFGqHpibrbduW37sH27YJcDUdHZ19+/Z5eHikpaX5+fmdPn2aFgp6Jrlcnp+fn5aWlp6enp6enpaWVlBQoPQ9lpaWMpns0aNHkydP/u2331ou0+fVTz/99OjRIzc3N1r+jxtcpfl54uPjgYv9Xc3J5XJbW1sA+O233zhs9kVx/DgBIE8WPQujuLiYnnulNBVUXV3d8kbXnEQioeuBw8PD4+Li6AKAliUwBePt7Q1cj+x4D2F5eTkAGBkZcfjLunDhAgBYWVnhYlFVTJ9OAIggZW2bS05OppX5Z8+evWrVKl9fX6VNiVSfPn28vLyWLVt24MCBrKys5z18v3btGi2BuWzZMsF+hLKyMnpijJr7oZUIsXaUbnHIzc3lqsH33nsPAJov5ENtVVNDOncmIhFhscaInvBDF9y3cqNro/Pnz9ORrWLlGt82btwIAD4+Ptw2K0QIvby8AODIkSOctMb3afJabs8eAkCeLHoWXnZ2dlhY2N/e6Npo586d8HQJTF7RigQcnghGCRHC5cuXA8CqVas4aS02NhYABg0axElrL5zx4wkAEerWIYDFixcDgJmZmTqVndoiLy9PJBIZGxtzviBWiDPr6QRpGkfnsjM4UlxrlJXBuXOgpweTJ7PuCmfWrFnj6+tbUVExYcKE+/fv83ehffv2EUL8/f25n4/lNtPPRJ/pcbJ4TVHp+TaHR7y/OL75hgAQ3pbysqIogcnroYL9+/cHAM5LthJh7oQODg4GBgaFhYUPHz5Us6kjR47U19ePHj26Zek09PeuXweRCLRuEGFgYBAbG2tpaZmQkEDPrlRNZWXl+fPnN2zYMHfu3DfffLP5l65cuXLjxo1evXpxs21CCeexfiZXV1cASEhIULMdelDh999/z0mvXkSFhRpznHw7Xb16lT4CCQsLa8v3/21tq+ZTtXTx3UcffcRHz4XYfAUALi4uKSkpaWlpo0aNatc/LCkpSUlJyc7OzsrKSk5OvnPnjr6+vr+/P0/91FplZRASAmZmYGEB//kPfPcddOsGvr7g6QknT7LuHDdeeeWV6OjoSZMmrVixwsbGhlYrbK6ysrL5WpysrKz6+vrm32BkZPTyyy+7uLg4Ozs7OzsrNgY0NTUdOnQIeJuJECiEbVy8Vltbm5mZmZaWRn9ZGRkZVVVVzb/BwsLi8uXLtOoOaocdO2DuXBg7FgICoKKCdW/44u3tHRYW9vHHH8+ZM8fGxoY+UVAYNGjQrVu3mn+mV69eTk5Ojo6Orq6urq6uAwYM0NXVbdks3TYxYMAAuhuBc8LdCeFZISwpKaF3uZSUlJSUlBs3bshksubfYGpqSn9H9Jc1ZMiQlsX50N8rLgZrawAACwsoKQEA2LkTzp+HnBy2/eLc0qVL8/Pzt23b5ufnd/nyZbrfgho5cqS5ubmLi4viXtfKq3lBQUH6E/Hx8SKRSOldIodEhJ/qA0oqKipeeuklIyOjS5cupaam0hFmWlqa0pyyWCx2cHBQvDgNGTKkV69eAnRP+4WHw+DB8OabMHkyfPcdxMRo33BUQSqVenl5nT171snJ6eLFi20ZNzU2Nubm5tI7QXZ29p9//vngwYPm3yAWiy0sLJKSkugxj9zi905YVFSkGIKLxeK6ujo6LlXo2bOns7Oz4pXJ0dFRIpHw2qUX1DvvQEgIxMbC0KFgasq6N/ySSCQxMTHu7u6ZmZnTp08/duxYy0Fmu4ZgdnZ2K1asuHLlip+f37lz5zjfuMPlnVDxckJ/vMuXLyvd6EQikZWVlYeHh7Oz86BBg5ydnelBAghxrqCgYMSIEWVlZYsWLfrqq6+a3+hUGILdvXt3+PDht27dmjJlCj2eicOuqhXCdr2cJCYm7t27d+XKlbRgI0J8S0hIGDdunFQqBQC5XN78SyoMwbKystzd3auqqj799FNaoYcr7QihVCrNyclR3OiuXLlSVlbW/BuUXk6cnJz69u2r+Gp0dPS0adMmTpz4008/cfgDINSKqKio/Pz8Tz/9tJW/zLY7efLkP//5T5lMtnfvXg4fV7Q1hJs3b164cKHSja579+7N55ocHR1b2TR97do1R0dHGxublnulEeJPY2MjALTyl9ku33777aJFi/T19ePj42mNY/W1NYTHjx/38/OzsrJSPFRp78uJTCYzMTGpr6+vqKjA+mhIcy1YsGDLli19+vS5nJ3d29hY/QbbGsKGhgaRSKTmy8nQoUOTk5MvXLjg4eGhTjsIMdTU1DRt5sxGb299V9fv+/fv/Kzn++3S1gXcnTp1Uv+GzndVfIQEIBaL9+zfb+jqml9fvyw/X6b28wUhdlEocLuxECFWDHV1v7a3N5NI/qiuDnt6KZwKBA0h3gmR1uitpxdhb6+voxP74MHBpx8TtJdAy9aohw8fmpmZGRoaVldX6+gImn+E+HC2snJ5fr4IYJ2d3auqTjcKmoSuXbv26dOntrY2Ly9PyOsixJOxpqbBvXvLAT4pKMh5emNU2wl9O8IRKdIyc3v18jIzq5PLP8rLK5dKVWhB6BDi3AzSMiKAlTY2rsbGTkZGqj2uEGg/oQLeCZH2kYhE39jZGejqqrasW+gQ4p0QaSVDXV0A+L26+ofS0v6GhgDwgYVFp7bNPgo6OwrNFq+Vl5ebavvGNvSi+b26uvDx4xk9erTrXwn9nlBXV9fJyYkQkpGRIfClERLA+crK9bdvf1tc3PZ/wuBhHU8nFiLUEbxuarrY0nJhe+riMgghzs0g1JzQEzOAczNIe7mZmLiZmLT3Xwk9MQNPFq/p6+s/evTomWUeEXqhMBiOdu3a1dLSsr6+HhevIQRMhqMAMHXq1OrqaqxuiBAwGY4ihJrD/UQIMYYhRIgxDCFCjGEIEWIMQ4gQYxhChBjDECLEGIYQIcYwhAgxhiFEiDEMIUKMYQgRYgxDiBBjGEKEGMMQIsQYhhAhxjCECDGGIUSIMQwhQoxhCBFiDEOIEGMYQoQYwxAixBiGECHGMIQIMYYhRIgxDCFCjGEIEWIMQ4gQYxhChBjDECLEGIYQIcYwhAgxhiFEiDEMIUKMYQgRYgxDiBBjGEKEGMMQIsQYhhAhxjCECDGGIUSIMQwhQoxhCBFiDEOIEGMYQoQYwxAixBiGECHGMIQIMYYhRIgxDCFCjGEIEWIMQ4gQYxhChBjDECLEGIYQIcYwhAgxhiFEiDEMIUKMYQgRYgxDiBBjGEKEGMMQIsQYhhAhxjCECDGGIUSIMQwhQoxhCBFiDEOIEGMYQoQYwxAixBiGECHGMIQIMfb/oMp68QF7A7IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LInArD_-eYur",
        "colab_type": "text"
      },
      "source": [
        "Let's now write these datasets to disk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lT7PxXreeYut",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "266c08eb-f5fa-46bc-ecc5-0d1c4943b5ac"
      },
      "source": [
        "print(\"Number of compounds in train set\")\n",
        "print(len(train_dataset))\n",
        "print(\"Number of compounds in validation set\")\n",
        "print(len(valid_dataset))\n",
        "print(\"Number of compounds in test set\")\n",
        "print(len(test_dataset))\n",
        "print(\"Number of compounds in crystal set\")\n",
        "print(len(crystal_dataset))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of compounds in train set\n",
            "204\n",
            "Number of compounds in validation set\n",
            "1273\n",
            "Number of compounds in test set\n",
            "45\n",
            "Number of compounds in crystal set\n",
            "25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "f8NYSeGdeYux",
        "colab_type": "text"
      },
      "source": [
        "The performance of common machine-learning algorithms can be very sensitive to preprocessing of the data. One common transformation applied to data is to normalize it to have zero-mean and unit-standard-deviation. We will apply this transformation to the pIC50 values (as seen above, the pIC50s range from 2 to 11)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKQfu5pveYuy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "7a824b8d-b432-49a8-c557-79185e66e316"
      },
      "source": [
        "transformers = [\n",
        "    dc.trans.NormalizationTransformer(transform_X=True, dataset=train_dataset),\n",
        "    dc.trans.ClippingTransformer(transform_X=True, dataset=train_dataset)]\n",
        "\n",
        "datasets = [train_dataset, valid_dataset, test_dataset, crystal_dataset]\n",
        "for i, dataset in enumerate(datasets):\n",
        "  for transformer in transformers:\n",
        "      datasets[i] = transformer.transform(dataset)\n",
        "train_dataset, valid_dataset, test_dataset, crystal_dataset = datasets"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/deepchem/trans/transformers.py:538: RuntimeWarning: invalid value encountered in true_divide\n",
            "  X = np.nan_to_num((X - self.X_means) / self.X_stds)\n",
            "/usr/local/lib/python3.6/dist-packages/deepchem/trans/transformers.py:538: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  X = np.nan_to_num((X - self.X_means) / self.X_stds)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shBrVTYGeYvA",
        "colab_type": "text"
      },
      "source": [
        "We now fit simple random forest models to our datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jU49euh3eYvC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# def rf_model_builder(model_params, model_dir):\n",
        "#   sklearn_model = RandomForestClassifier(**model_params)\n",
        "#   return dc.models.SklearnModel(sklearn_model, model_dir)\n",
        "# params_dict = {\n",
        "#     \"n_estimators\": [10, 100],\n",
        "#     \"max_features\": [\"auto\", \"sqrt\", \"log2\", None],\n",
        "# }\n",
        "\n",
        "# metric = dc.metrics.Metric(dc.metrics.roc_auc_score)\n",
        "# optimizer = dc.hyper.HyperparamOpt(rf_model_builder)\n",
        "# best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(\n",
        "#     params_dict, train_dataset, valid_dataset, transformers,\n",
        "#     metric=metric)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jqjBgMxHeYvO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import numpy.random\n",
        "\n",
        "# params_dict = {\"learning_rate\": np.power(10., np.random.uniform(-5, -3, size=1)),\n",
        "#                \"weight_decay_penalty\": np.power(10, np.random.uniform(-6, -4, size=1)),\n",
        "#                \"nb_epoch\": [40] }\n",
        "# n_features = train_dataset.get_data_shape()[0]\n",
        "# def model_builder(model_params, model_dir):\n",
        "#   model = dc.models.MultitaskClassifier(\n",
        "#     1, n_features, layer_sizes=[1000], dropouts=.25,\n",
        "#     batch_size=50, **model_params)\n",
        "#   return model\n",
        "\n",
        "# optimizer = dc.hyper.HyperparamOpt(model_builder)\n",
        "# best_dnn, best_dnn_hyperparams, all_dnn_results = optimizer.hyperparam_search(\n",
        "#     params_dict, train_dataset, valid_dataset, transformers,\n",
        "#     metric=metric)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vhsHoeLeYvU",
        "colab_type": "text"
      },
      "source": [
        "Now let's evaluate the best model on the validation and test sets and save the results to csv."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VeINkC9ReYvW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from deepchem.utils.evaluate import Evaluator\n",
        "\n",
        "# rf_train_csv_out = \"rf_train_regressor.csv\"\n",
        "# rf_train_stats_out = \"rf_train_stats_regressor.txt\"\n",
        "# rf_train_evaluator = Evaluator(best_rf, train_dataset, transformers)\n",
        "# rf_train_score = rf_train_evaluator.compute_model_performance(\n",
        "#     [metric], rf_train_csv_out, rf_train_stats_out)\n",
        "# print(\"RF Train set AUC %f\" % (rf_train_score[\"roc_auc_score\"]))\n",
        "\n",
        "# rf_valid_csv_out = \"rf_valid_regressor.csv\"\n",
        "# rf_valid_stats_out = \"rf_valid_stats_regressor.txt\"\n",
        "# rf_valid_evaluator = Evaluator(best_rf, valid_dataset, transformers)\n",
        "# rf_valid_score = rf_valid_evaluator.compute_model_performance(\n",
        "#     [metric], rf_valid_csv_out, rf_valid_stats_out)\n",
        "# print(\"RF Valid set AUC %f\" % (rf_valid_score[\"roc_auc_score\"]))\n",
        "\n",
        "# rf_test_csv_out = \"rf_test_regressor.csv\"\n",
        "# rf_test_stats_out = \"rf_test_stats_regressor.txt\"\n",
        "# rf_test_evaluator = Evaluator(best_rf, test_dataset, transformers)\n",
        "# rf_test_score = rf_test_evaluator.compute_model_performance(\n",
        "#     [metric], rf_test_csv_out, rf_test_stats_out)\n",
        "# print(\"RF Test set AUC %f\" % (rf_test_score[\"roc_auc_score\"]))\n",
        "\n",
        "# rf_crystal_csv_out = \"rf_crystal_regressor.csv\"\n",
        "# rf_crystal_stats_out = \"rf_crystal_stats_regressor.txt\"\n",
        "# rf_crystal_evaluator = Evaluator(best_rf, crystal_dataset, transformers)\n",
        "# rf_crystal_score = rf_crystal_evaluator.compute_model_performance(\n",
        "#     [metric], rf_crystal_csv_out, rf_crystal_stats_out)\n",
        "# print(\"RF Crystal set R^2 %f\" % (rf_crystal_score[\"roc_auc_score\"]))"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMDBBUtJeYvb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dnn_train_csv_out = \"dnn_train_classifier.csv\"\n",
        "# dnn_train_stats_out = \"dnn_train_classifier_stats.txt\"\n",
        "# dnn_train_evaluator = Evaluator(best_dnn, train_dataset, transformers)\n",
        "# dnn_train_score = dnn_train_evaluator.compute_model_performance(\n",
        "#     [metric], dnn_train_csv_out, dnn_train_stats_out)\n",
        "# print(\"DNN Train set AUC %f\" % (dnn_train_score[\"roc_auc_score\"]))\n",
        "\n",
        "# dnn_valid_csv_out = \"dnn_valid_classifier.csv\"\n",
        "# dnn_valid_stats_out = \"dnn_valid_classifier_stats.txt\"\n",
        "# dnn_valid_evaluator = Evaluator(best_dnn, valid_dataset, transformers)\n",
        "# dnn_valid_score = dnn_valid_evaluator.compute_model_performance(\n",
        "#     [metric], dnn_valid_csv_out, dnn_valid_stats_out)\n",
        "# print(\"DNN Valid set AUC %f\" % (dnn_valid_score[\"roc_auc_score\"]))\n",
        "\n",
        "# dnn_test_csv_out = \"dnn_test_classifier.csv\"\n",
        "# dnn_test_stats_out = \"dnn_test_classifier_stats.txt\"\n",
        "# dnn_test_evaluator = Evaluator(best_dnn, test_dataset, transformers)\n",
        "# dnn_test_score = dnn_test_evaluator.compute_model_performance(\n",
        "#     [metric], dnn_test_csv_out, dnn_test_stats_out)\n",
        "# print(\"DNN Test set AUC %f\" % (dnn_test_score[\"roc_auc_score\"]))\n",
        "\n",
        "# dnn_crystal_csv_out = \"dnn_crystal_classifier.csv\"\n",
        "# dnn_crystal_stats_out = \"dnn_crystal_stats_classifier.txt\"\n",
        "# dnn_crystal_evaluator = Evaluator(best_dnn, crystal_dataset, transformers)\n",
        "# dnn_crystal_score = dnn_crystal_evaluator.compute_model_performance(\n",
        "#     [metric], dnn_crystal_csv_out, dnn_crystal_stats_out)\n",
        "# print(\"DNN Crystal set AUC %f\" % (dnn_crystal_score[\"roc_auc_score\"]))"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjflxuMMeYvf",
        "colab_type": "text"
      },
      "source": [
        "Now, we construct regression models for the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqEbvd2ZeYvg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #Make directories to store the raw and featurized datasets.\n",
        "# featurizer = dc.feat.UserDefinedFeaturizer(user_specified_features)\n",
        "# loader = dc.data.UserCSVLoader(\n",
        "#     tasks=[\"pIC50\"], smiles_field=\"mol\", id_field=\"CID\",\n",
        "#     featurizer=featurizer)\n",
        "# dataset = loader.featurize(dataset_file)\n",
        "# crystal_dataset = loader.featurize(crystal_dataset_file)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPEHZbTreYvo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# splitter = dc.splits.SpecifiedSplitter(dataset_file, \"Model\")\n",
        "# train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(\n",
        "#     dataset)\n",
        "# #NOTE THE RENAMING:\n",
        "# valid_dataset, test_dataset = test_dataset, valid_dataset"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "leu2sy1HeYvx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# print(\"Number of compounds in train set\")\n",
        "# print(len(train_dataset))\n",
        "# print(\"Number of compounds in validation set\")\n",
        "# print(len(valid_dataset))\n",
        "# print(\"Number of compounds in test set\")\n",
        "# print(len(test_dataset))\n",
        "# print(\"Number of compounds in crystal set\")\n",
        "# print(len(crystal_dataset))"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NmlQz-9ZeYv2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# transformers = [\n",
        "#     dc.trans.NormalizationTransformer(transform_X=True, dataset=train_dataset),\n",
        "#     dc.trans.ClippingTransformer(transform_X=True, dataset=train_dataset)]\n",
        "\n",
        "# datasets = [train_dataset, valid_dataset, test_dataset, crystal_dataset]\n",
        "# for i, dataset in enumerate(datasets):\n",
        "#   for transformer in transformers:\n",
        "#       datasets[i] = transformer.transform(dataset)\n",
        "# train_dataset, valid_dataset, test_dataset, crystal_dataset = datasets"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgB88N9leYv7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "# def rf_model_builder(model_params, model_dir):\n",
        "#   sklearn_model = RandomForestRegressor(**model_params)\n",
        "#   return dc.models.SklearnModel(sklearn_model, model_dir)\n",
        "# params_dict = {\n",
        "#     \"n_estimators\": [10, 100],\n",
        "#     \"max_features\": [\"auto\", \"sqrt\", \"log2\", None],\n",
        "# }\n",
        "\n",
        "# metric = dc.metrics.Metric(dc.metrics.r2_score)\n",
        "# optimizer = dc.hyper.HyperparamOpt(rf_model_builder)\n",
        "# best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(\n",
        "#     params_dict, train_dataset, valid_dataset, transformers,\n",
        "#     metric=metric)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEhs3pUueYv_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import numpy.random\n",
        "\n",
        "# params_dict = {\"learning_rate\": np.power(10., np.random.uniform(-5, -3, size=2)),\n",
        "#                \"weight_decay_penalty\": np.power(10, np.random.uniform(-6, -4, size=2)),\n",
        "#                \"nb_epoch\": [20] }\n",
        "# n_features = train_dataset.get_data_shape()[0]\n",
        "# def model_builder(model_params, model_dir):\n",
        "#   model = dc.models.MultitaskRegressor(\n",
        "#     1, n_features, layer_sizes=[1000], dropouts=[.25],\n",
        "#     batch_size=50, **model_params)\n",
        "#   return model\n",
        "\n",
        "# optimizer = dc.hyper.HyperparamOpt(model_builder)\n",
        "# best_dnn, best_dnn_hyperparams, all_dnn_results = optimizer.hyperparam_search(\n",
        "#     params_dict, train_dataset, valid_dataset, transformers,\n",
        "#     metric=metric)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1c-1CX5weYwC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from deepchem.utils.evaluate import Evaluator\n",
        "\n",
        "# rf_train_csv_out = \"rf_train_regressor.csv\"\n",
        "# rf_train_stats_out = \"rf_train_stats_regressor.txt\"\n",
        "# rf_train_evaluator = Evaluator(best_rf, train_dataset, transformers)\n",
        "# rf_train_score = rf_train_evaluator.compute_model_performance(\n",
        "#     [metric], rf_train_csv_out, rf_train_stats_out)\n",
        "# print(\"RF Train set R^2 %f\" % (rf_train_score[\"r2_score\"]))\n",
        "\n",
        "# rf_valid_csv_out = \"rf_valid_regressor.csv\"\n",
        "# rf_valid_stats_out = \"rf_valid_stats_regressor.txt\"\n",
        "# rf_valid_evaluator = Evaluator(best_rf, valid_dataset, transformers)\n",
        "# rf_valid_score = rf_valid_evaluator.compute_model_performance(\n",
        "#     [metric], rf_valid_csv_out, rf_valid_stats_out)\n",
        "# print(\"RF Valid set R^2 %f\" % (rf_valid_score[\"r2_score\"]))\n",
        "\n",
        "# rf_test_csv_out = \"rf_test_regressor.csv\"\n",
        "# rf_test_stats_out = \"rf_test_stats_regressor.txt\"\n",
        "# rf_test_evaluator = Evaluator(best_rf, test_dataset, transformers)\n",
        "# rf_test_score = rf_test_evaluator.compute_model_performance(\n",
        "#     [metric], rf_test_csv_out, rf_test_stats_out)\n",
        "# print(\"RF Test set R^2 %f\" % (rf_test_score[\"r2_score\"]))\n",
        "\n",
        "# rf_crystal_csv_out = \"rf_crystal_regressor.csv\"\n",
        "# rf_crystal_stats_out = \"rf_crystal_stats_regressor.txt\"\n",
        "# rf_crystal_evaluator = Evaluator(best_rf, crystal_dataset, transformers)\n",
        "# rf_crystal_score = rf_crystal_evaluator.compute_model_performance(\n",
        "#     [metric], rf_crystal_csv_out, rf_crystal_stats_out)\n",
        "# print(\"RF Crystal set R^2 %f\" % (rf_crystal_score[\"r2_score\"]))"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7g92mUweYwF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dnn_train_csv_out = \"dnn_train_regressor.csv\"\n",
        "# dnn_train_stats_out = \"dnn_train_regressor_stats.txt\"\n",
        "# dnn_train_evaluator = Evaluator(best_dnn, train_dataset, transformers)\n",
        "# dnn_train_score = dnn_train_evaluator.compute_model_performance(\n",
        "#     [metric], dnn_train_csv_out, dnn_train_stats_out)\n",
        "# print(\"DNN Train set R^2 %f\" % (dnn_train_score[\"r2_score\"]))\n",
        "\n",
        "# dnn_valid_csv_out = \"dnn_valid_regressor.csv\"\n",
        "# dnn_valid_stats_out = \"dnn_valid_regressor_stats.txt\"\n",
        "# dnn_valid_evaluator = Evaluator(best_dnn, valid_dataset, transformers)\n",
        "# dnn_valid_score = dnn_valid_evaluator.compute_model_performance(\n",
        "#     [metric], dnn_valid_csv_out, dnn_valid_stats_out)\n",
        "# print(\"DNN Valid set R^2 %f\" % (dnn_valid_score[\"r2_score\"]))\n",
        "\n",
        "# dnn_test_csv_out = \"dnn_test_regressor.csv\"\n",
        "# dnn_test_stats_out = \"dnn_test_regressor_stats.txt\"\n",
        "# dnn_test_evaluator = Evaluator(best_dnn, test_dataset, transformers)\n",
        "# dnn_test_score = dnn_test_evaluator.compute_model_performance(\n",
        "#     [metric], dnn_test_csv_out, dnn_test_stats_out)\n",
        "# print(\"DNN Test set R^2 %f\" % (dnn_test_score[\"r2_score\"]))\n",
        "\n",
        "# dnn_crystal_csv_out = \"dnn_crystal_regressor.csv\"\n",
        "# dnn_crystal_stats_out = \"dnn_crystal_stats_regressor.txt\"\n",
        "# dnn_crystal_evaluator = Evaluator(best_dnn, crystal_dataset, transformers)\n",
        "# dnn_crystal_score = dnn_crystal_evaluator.compute_model_performance(\n",
        "#     [metric], dnn_crystal_csv_out, dnn_crystal_stats_out)\n",
        "# print(\"DNN Crystal set R^2 %f\" % (dnn_crystal_score[\"r2_score\"]))\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPpZmZbqeYwK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# task = \"pIC50\"\n",
        "# rf_predicted_test = best_rf.predict(test_dataset)\n",
        "# rf_true_test = test_dataset.y\n",
        "# plt.scatter(rf_predicted_test, rf_true_test)\n",
        "# plt.xlabel('Predicted pIC50s')\n",
        "# plt.ylabel('Secondary Assay')\n",
        "# plt.title(r'RF predicted IC50 vs. Secondary Assay')\n",
        "# plt.xlim([2, 11])\n",
        "# plt.ylim([2, 11])\n",
        "# plt.plot([2, 11], [2, 11], color='k')\n",
        "# plt.show()"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBCPydPleYwO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# task = \"pIC50\"\n",
        "# dnn_predicted_test = best_dnn.predict(test_dataset, transformers)\n",
        "# dnn_true_test = test_dataset.y\n",
        "# plt.scatter(dnn_predicted_test, dnn_true_test)\n",
        "# plt.xlabel('Predicted pIC50s')\n",
        "# plt.ylabel('Secondary Assay')\n",
        "# plt.title(r'DNN predicted IC50 vs. Secondary Assay')\n",
        "# plt.xlim([2, 11])\n",
        "# plt.ylim([2, 11])\n",
        "# plt.plot([2, 11], [2, 11], color='k')\n",
        "# plt.show()"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwSpFWsPeYwS",
        "colab_type": "text"
      },
      "source": [
        "# Congratulations! Time to join the Community!\n",
        "\n",
        "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
        "\n",
        "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
        "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
        "\n",
        "## Join the DeepChem Gitter\n",
        "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
      ]
    }
  ]
}